Comparison of data-driven Takagi–Sugeno models of rainfall–discharge dynamics

Over the last decades, several data-driven techniques have been applied to model the rainfall–discharge dynamics of catchments. Among these techniques are fuzzy rule-based models, which attempt to describe the catchment response to rainfall input through fuzzy relationships. In this paper, we demonstrate three different methods for constructing fuzzy rule-based models of the Takagi–Sugeno type relating rainfall to catchment discharge. They correspond to the grid partitioning, subtractive clustering, and Gustafson–Kessel (GK) clustering identification methods. The data set used to parametrize and validate the models consists of hourly precipitation and discharge records. The models are parametrized using a 1-year identification data set and are then applied to a 4-year data set. Although the models show a similar performance, the best results are obtained for the GK method. A real-time flood forecasting algorithm is then developed, in which discharge measurements are assimilated into the model at either an hourly or a daily time step. The results suggest that the GK method can potentially be used as an operational flood forecasting tool with a low computational cost.